ggplot2

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# Data vizualisation using _ggplot2_

### Maximilian H.K. Hesselbarth

#### University of Michigan (EEB)

2022-03-25

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# Grammar of Graphics

- Based on Grammar of Graphics

.ref[Wilkinson, L., 2012. The grammar of graphics. In Handbook of computational statistics (pp. 375-414). Springer, Berlin, Heidelberg.]

- Elements of all _ggplots_:

1. **data** Information to visualize
  
  2. **aesthetic** Mappings of data to plot coordinates (position, color, size, ...)
  
  3. **geometry** Shapes to represent the data (points, lines, polygons, ...)

.ref[Wickham, H., 2016. ggplot2: Elegant graphics for data analysis. Springer, New York, USA.]  
.ref[Illustration from the Openscapes blog Tidy Data for reproducibility, efficiency, and collaboration by Julia Lowndes and Allison Horst]

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# Data and mapping layer

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- Map body mass to **x** axis and bill length to **y** axis

```r
ggplot(data = penguins, 
  mapping = aes(x = body_mass_g, 
                y = bill_length_mm))
```

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# Geometry layer

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- Adding a geometric **point** layer

```r
ggplot(data = penguins, 
  mapping = aes(x = body_mass_g, 
                y = bill_length_mm)) + 
* geom_point()
```

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# Advanced mapping layer

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- Add *color* mapping and change **size** and **shape**

```r
ggplot(data = penguins, 
  mapping = aes(x = body_mass_g, 
                y = bill_length_mm)) + 
* geom_point(aes(col = species),
*            size = 2.5, shape = 1)
```

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# Scaling layer

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- **Scaling** relates data values space to aesthetic values space

```r
ggplot(data = penguins, 
  mapping = aes(x = body_mass_g, 
                y = bill_length_mm)) + 
  geom_point(aes(col = species), 
             size = 2.5, shape = 1) +
* scale_color_viridis_d(option = "C")
```

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# Using different themes

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- Customize layout using **themes**

```r
ggplot(data = penguins, 
  mapping = aes(x = body_mass_g, 
                y = bill_length_mm)) + 
  geom_point(aes(col = species), 
             size = 2.5, shape = 1) +
  scale_color_viridis_d(option = "C") + 
  labs(x = "Body mass [cm]", y = "Bill length [mm]",
       title = "Body - Bill Relationship") + 
* theme_bw() +
* theme(legend.position = "bottom",
*       plot.title = element_text(
*         face = "bold", size = 16))
```

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# Adding geoms

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- Adding further **geometries**

```r
ggplot(data = penguins, 
  mapping = aes(x = body_mass_g, 
                y = bill_length_mm)) + 
  geom_point(aes(col = species), 
             size = 2.5, shape = 1) +
* geom_smooth(aes(col = species),
*             method = "lm", se = FALSE) +
* geom_label(data = df_text,
*   aes(x = mass_mean, y = bill_mean,
*       label = species)) +
  scale_color_viridis_d(option = "C") + 
  labs(x = "Body mass [cm]", y = "Bill length [mm]",
       title = "Body - Bill Relationship") + 
  theme_bw() +
* theme(legend.position = "none",
        plot.title = element_text(
          face = "bold", size = 16))
```

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<img src="data:image/png;base64,#ggplot2_files/figure-html/unnamed-chunk-12-1.png" width="100%" style="display: block; margin: auto;" />
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# Facet wrapping

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- Split into **panels** using grouping

```r
ggplot(data = penguins, 
  mapping = aes(x = body_mass_g, 
                y = bill_length_mm)) + 
  geom_point(aes(col = species), 
             size = 2.5, shape = 1) +
  geom_smooth(aes(col = species),
              method = "lm", se = FALSE) +
* facet_wrap(. ~ island) +
  scale_color_viridis_d(option = "C") + 
  labs(x = "Body mass [cm]", y = "Bill length [mm]",
       title = "Body - Bill Relationship") + 
  theme_bw() +
  theme(legend.position = "bottom",
        plot.title = element_text(
          face = "bold", size = 16))
```

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# Combining figures

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- Figures can be stored in **variables**

```r
p1 <- ggplot(penguins, 
  aes(x = species, body_mass_g)) + 
  geom_jitter()

p2 <- ggplot(penguins, 
  aes(x = species, body_mass_g)) + 
  geom_boxplot()

p3 <- ggplot(penguins, 
  aes(x = species, body_mass_g)) +
  geom_violin()
```

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```r
library(cowplot)
cowplot::plot_grid(p1, p2, p3, nrow = 3)
```

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.ref[Illustration from the Openscapes blog Tidy Data for reproducibility, efficiency, and collaboration by Julia Lowndes and Allison Horst]

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# Animated figures

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- `gganimate` allows all kind of **dynamic** figures

```r
library(gganimate)

p <- ggplot(penguins, 
    aes(x = body_mass_g, 
        y = bill_length_mm)) + 
  geom_point(aes(color = species)) + 
  geom_label(data = df_text, 
    aes(x = mass_mean, y = bill_mean, 
        label = species)) +
  scale_color_viridis_d(option = "C") + 
* transition_states(states = species,
*   transition_length = 2,
*   state_length = 1) +
* enter_fade() +
* exit_fade() +
  theme_classic() + 
  theme(legend.position = "none")
```

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```r
gganimate::animate(p)
```

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## Thank you for your attention

### Questions?

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Further resources: [https://mhesselbarth.github.io/advanced-r-workshop/resources](https://mhesselbarth.github.io/advanced-r-workshop/resources)

Exercise: [https://mhesselbarth.github.io/advanced-r-workshop/exercise_ggplot2](https://mhesselbarth.github.io/advanced-r-workshop/resources)

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